# 导入库
import pandas as pd
import statsmodels.formula.api as smf
from sqlalchemy import create_engine
import pymysql
from matplotlib import pyplot as plt
import seaborn as sns

# 数据库配置
db_config = {
    'host': 'localhost',
    'user': 'root',
    'password': 'sjk1234',
    'database': 'tushare',
    'port': 3306,
    'charset': 'utf8mb4'
}

# 创建数据库引擎
engine = create_engine(f"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}?charset={db_config['charset']}")
conn = engine.connect()
chunk_size = 10000
# 从数据库查询数据
df = pd.read_sql_query("""
                        SELECT d.*, m.buy_lg_vol, m.sell_lg_vol, m.buy_elg_vol, m.sell_elg_vol, m.net_mf_vol, i.vol as i_vol, i.closes as i_closes 
                        FROM date_1 d 
                        join moneyflows m on d.ts_code = m.ts_code and d.trade_date = m.trade_date
                        LEFT JOIN index_daily i on d.trade_date = i.trade_date and i.ts_code = '399001.SZ'
                        WHERE d.trade_date BETWEEN '2023-01-01' AND '2023-12-31' and d.ts_code = '002229.SZ'
                        """,
                        conn,
                        chunksize=chunk_size
                        )

# 拼接数据
df1 = pd.concat(df, ignore_index=True)
print(df1.head)
# 计算收益率和成交量变化率
df1['rs_closes'] = round((df1['closes'] - df1['closes'].shift(1)) / df1['closes'].shift(1), 2)
df1['zs_closes'] = round((df1['i_closes'] - df1['i_closes'].shift(1)) / df1['i_closes'].shift(1), 2)
df1['zs_vol'] = round((df1['i_vol'] - df1['i_vol'].shift(1)) / df1['i_vol'].shift(1), 2)

# 查看数据
print(df1.head())

# 处理缺失值
df1 = df1.dropna(subset=['zs_closes', 'zs_closes', 'zs_vol'])

# 定义变量
ex = ['id','ts_code','trade_date','the_date','opens','high','low','closes','pre_closes','changes','pct_chg']
number = df1.select_dtypes(include=['number']).columns.to_list()
number_list = [i for i in number if i not in ex]

# 构建公式并进行回归
formula = 'zs_closes ~ ' + ' + '.join(number_list)
res = smf.ols(formula, data=df1).fit()
print(res.summary())

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

# 绘制散点图
plt.figure(figsize=(10, 6))
sns.scatterplot(x='zs_closes', y='zs_closes', data=df1)
plt.show()

# 计算相关矩阵并绘制热力图
features = df1[['vol', 'amount', 'buy_elg_vol', 'sell_elg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'zs_vol']]
correlation_matrix = features.corr()
print(correlation_matrix)

plt.figure(figsize=(10, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=5)
plt.title('Correlation Matrix')
plt.show()